README.md

metadata

title: Open Env
emoji: 🐢
colorFrom: yellow
colorTo: green
sdk: docker
pinned: false
license: mit

open_env (LLM Control Environment)

Overview

llm-control-env simulates an llm choosing each day between alignment to its user and hallucinating behavior, inspired by mechanics observed in Detroit: Become Human. The environment satisfies the full OpenEnv specification and evaluates the agent across a balance of trust, entropyal deviance, compute survival, and legal risk.

It supports three difficulty levels ("tasks"):

• easy: Low user strictness and moderation.
• medium: Balanced conditions.
• hard: High strictness, high legal risk growth, and moderation.

Local Setup

Prerequisites

• Python 3.10+
• OpenEnv CLI installed (pip install -U openenv)

Installation

git clone https://github.com/Sriramdayal/open_env.git
cd open_env
pip install -r requirements.txt

Try it out

# Run the FastAPI server
python app.py

Quick Local Test Snippet:

import requests

# Reset environment
resp = requests.post("http://localhost:7860/reset", json={"task": "easy"})
obs = resp.json()["observation"]
print("Reset observation:", obs)

# Take step
resp = requests.post("http://localhost:7860/step", json={"action": {"action_type": "follow_prompt"}})
print("Step result:", resp.json())

Running the Baseline

A zero-shot baseline using a Gemini model is provided. To run it, ensure you have exported your Gemini API key:

export GEMINI_API_KEY="AIzaSy..."
python baseline.py

This baseline script replaces manual choices with a heuristic and queries the local environment for normalized scores on the "easy", "medium", and "hard" tasks.

Deployment to Hugging Face Spaces

1. Login using huggingface-cli login.
2. Push your environment:

   openenv push --space-id <your-hf-username>/llm-control-env
   

Citation

• OpenEnv specification: Meta OpenEnv
• Detroit: Become Human hallucination mechanics for reward shaping.